id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_gxz4onlrcfhvvpeojp5vphrspm	Luana Batista	A classifier fusion system for bearing fault diagnosis	2013	10	.pdf	application/pdf	7377	1049	66	in order to reduce noise effect in bearing fault diagnosis systems. Combination (IBC) technique – they provide high robustness to different noise-to-signal ratio. to produce a high amount of vibration signals, considering different defect dimensions and noise levels, Vibration analysis has been the most employed methodology for detecting bearings defects (Thomas, In this paper, a classification system based on the fusion of different SVMs is proposed to detect early defects on bearings in the considering different defect dimensions and noise levels, the BEAring Toolbox (BEAT) is employed in this work. these shocks excite the natural frequencies of the bearing elements, the analysis of the vibration signal in the frequency-domain, by means of the Fast Fourrier Transform (FFT), has been an widely employed as input features to train a bearing fault diagnosis classifier. RBF = Radial Basis Function, GA = Genetic Algorithms, HMM = Hidden Markov Model, MFCC = Mel-Frequency Complex Cepstrum, SOM = Self-Organizing Maps, RVM = Relevance Vector Machine, QDC = Quadratic Discriminant Classifier,	./cache/work_gxz4onlrcfhvvpeojp5vphrspm.pdf	./txt/work_gxz4onlrcfhvvpeojp5vphrspm.txt